Wired to Reason: Connectome Reservoirs Align with Vision-Language Models on Raven's Matrices

The human brain and large neural networks have independently converged on surprisingly similar internal representations, a finding typically attributed to learning at scale. But what if a part of this convergence requires no gradient learning in the recurrent operators? We test this using a connectome-wired reservoir: a recurrent dynamical system whose bilinear operators are derived entirely from empirical brain connectivity matrices, with no learning in the recurrent weights. Remark- ably, the internal states this system builds during visual reasoning align with the layer-wise representations of a trained vision-language model in a structured and interpretable way: alignment varies systematically across recurrent states and VLM depth, with stronger alignment for correct trials in several model–metric combinations, without gradient-based optimization. This alignment is not a statistical artifact: empirical FC exhibits modest but consistently stronger alignment than the matched random control, while temporal-memory removal alters the alignment structure. Moreover, reservoirs instantiated with connectomes from both neurotypical and autistic individuals produce consistent visual reasoning performance that substantially exceeds random-weight baselines, indicating that this advantage generalizes across both cohorts rather than being tied to any particular population or diagnostic profile; structure-matched null controls further indicate that the empiri- cal connectome’s regional arrangement, not any single isolated property, underlies the alignment advantage. Taken together, our results suggest that the partial convergence between biological and vision-language models (VLMs) is not exclusively learned: FC-derived recurrent organization provides a structural inductive bias for partial CWR–VLM representational correspondence. Here, connectome-wired refers specifically to the use of an empirical brain connectivity matrix to determine the fixed recurrent dynamics of the reservoir. No measured neural activity is used as a representation target: all alignment analyses compare reservoir states with representations from trained vision-language models.